Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity

Reconstructing complex and dynamic visual perception from brain activity remains a major challenge in machine learning applications to neuroscience. Here we present a new method for reconstructing naturalistic images and videos from very large single-participant functional magnetic resonance data that leverages the recent success of image-to-image transformation networks. This is achieved by exploiting spatial information obtained from retinotopic mappings across the visual system. More specifically, we first determine what position each voxel in a particular region of interest would represent in the visual field based on its corresponding receptive field location. Then, the 2D image representation of the brain activity on the visual field is passed to a fully convolutional image-to-image network trained to recover the original stimuli using VGG feature loss with an adversarial regularizer. In our experiments, we show that our method offers a significant improvement over existing video reconstruction technique

HIV-1 latency in actively dividing human T cell lines

<p>Abstract</p> <p>Background</p> <p>Eradication of HIV-1 from an infected individual cannot be achieved by current drug regimens. Viral reservoirs established early during the infection remain unaffected by anti-retroviral therapy and are able to replenish systemic infection upon interruption of the treatment. Therapeutic targeting of viral latency will require a better understanding of the basic mechanisms underlying the establishment and long-term maintenance of HIV-1 in resting memory CD4 T cells, the most prominent reservoir of transcriptional silent provirus. However, the molecular mechanisms that permit long-term transcriptional control of proviral gene expression in these cells are still not well understood. Exploring the molecular details of viral latency will provide new insights for eventual future therapeutics that aim at viral eradication.</p> <p>Results</p> <p>We set out to develop a new in vitro HIV-1 latency model system using the doxycycline (dox)-inducible HIV-rtTA variant. Stable cell clones were generated with a silent HIV-1 provirus, which can subsequently be activated by dox-addition. Surprisingly, only a minority of the cells was able to induce viral gene expression and a spreading infection, eventhough these experiments were performed with the actively dividing SupT1 T cell line. These latent proviruses are responsive to TNFα treatment and alteration of the DNA methylation status with 5-Azacytidine or genistein, but not responsive to the regular T cell activators PMA and IL2. Follow-up experiments in several T cell lines and with wild-type HIV-1 support these findings.</p> <p>Conclusion</p> <p>We describe the development of a new in vitro model for HIV-1 latency and discuss the advantages of this system. The data suggest that HIV-1 proviral latency is not restricted to resting T cells, but rather an intrinsic property of the virus.</p

Latency profiles of full length HIV-1 molecular clone variants with a subtype specific promoter

<p>Abstract</p> <p>Background</p> <p>HIV-1 transcription initiation depends on cellular transcription factors that bind to promoter sequences in the Long Terminal Repeat (LTR). Each HIV-1 subtype has a specific LTR promoter configuration and even minor sequence changes in the transcription factor binding sites (TFBS) or their arrangement can impact transcriptional activity. Most latency studies have focused on HIV-1 subtype B strains, and the degree to which LTR promoter variation contributes to differences in proviral latency is therefore largely unknown. Latency differences may influence establishment and size of viral reservoirs as well as the possibility to clear the virus by therapeutic intervention.</p> <p>Results</p> <p>We investigated the proviral transcriptional latency properties of different HIV-1 subtypes as their LTRs have unique assemblies of transcription factor binding sites. We constructed recombinant viral genomes with the subtype-specific promoters inserted in the common backbone of the subtype B LAI isolate. The recombinant viruses are isogenic, except for the core promoter region that encodes all major TFBS, including NFκB and Sp1 sites. We developed and optimized an assay to investigate HIV-1 proviral latency in T cell lines. Our data show that the majority of HIV-1 infected T cells only start viral gene expression after TNFα activation.</p> <p>Conclusions</p> <p>There were no gross differences among the subtypes, both in the initial latency level and the activation response, except for subtype AE that combines an increased level of basal transcription with a reduced TNFα response. This subtype AE property is related to the presence of a GABP instead of NFκB binding site in the LTR.</p

Adverse events related to low dose corticosteroids in autoimmune hepatitis

Background: Autoimmune hepatitis requires long-term therapy, and systemic corticosteroids are the backbone of therapeutic management. Prolonged use of corticosteroids may lead to adverse events but data from long-term studies are mainly derived from studies in rheumatic diseases. Aim: To assess cataract, diabetes and fractures in relation to corticosteroid doses in the long-term maintenance treatment of patients with autoimmune hepatitis. Methods: We retrospectively collected data on 476 patients (77% women) with an established diagnosis of autoimmune hepatitis. Binary logistic regression with a generalised estimating equation was used to analyse the association between current corticosteroid use and the incidence of cataract, diabetes and fractures with onset after autoimmune hepatitis diagnosis. We corrected for sex, age, cirrhosis at diagnosis and predniso(lo)ne use in the prior 3 years to account for possible ongoing effects. Results: A total of 6634 years, with a median of 13 (range 1-40) per patient were recorded. The median age at diagnosis was 44 years (range 2-88). Adverse events were documented in 120 (25%) patients. Low-dose predniso(lo)ne (0.1-5.0 mg/d) increased the odds of fractures whereas higher doses (>5.0 mg/d) increased the odds of cataracts and diabetes. Budesonide increased the odds of cataract and fractures; this effect was independent of predniso(lo)ne use in the prior 1, 2 or 3 years. Conclusions: Even low doses of corticosteroids frequently lead to substantial adverse events refuting the assumption that adverse events are prevented by administering low doses

FABM-PCLake – linking aquatic ecology with hydrodynamics

This study presents FABM-PCLake, a redesigned structure of the PCLake aquatic ecosystem model, which we implemented in the Framework for Aquatic Biogeochemical Models (FABM). In contrast to the original model, which was designed for temperate, fully mixed freshwater lakes, the new FABM-PCLake represents an integrated aquatic ecosystem model that can be linked with different hydrodynamic models and allows simulations of hydrodynamic and biogeochemical processes for zero-dimensional, one-dimensional as well as three-dimensional environments. FABM-PCLake describes interactions between multiple trophic levels, including piscivorous, zooplanktivorous and benthivorous fish, zooplankton, zoobenthos, three groups of phytoplankton and rooted macrophytes. The model also accounts for oxygen dynamics and nutrient cycling for nitrogen, phosphorus and silicon, both within the pelagic and benthic domains. FABM-PCLake includes a two-way communication between the biogeochemical processes and the physics, where some biogeochemical state variables (e.g., phytoplankton) influence light attenuation and thereby the spatial and temporal distributions of light and heat. At the same time, the physical environment, including water currents, light and temperature influence a wide range of biogeochemical processes. The model enables studies on ecosystem dynamics in physically heterogeneous environments (e.g., stratifying water bodies, and water bodies with horizontal gradients in physical and biogeochemical properties), and through FABM also enables data assimilation and multi-model ensemble simulations. Examples of potential new model applications include climate change impact studies and environmental impact assessment scenarios for temperate, sub-tropical and tropical lakes and reservoirs

On Form Factors in nested Bethe Ansatz systems

We investigate form factors of local operators in the multi-component Quantum Non-linear Schr\"odinger model, a prototype theory solvable by the so-called nested Bethe Ansatz. We determine the analytic properties of the infinite volume form factors using the coordinate Bethe Ansatz solution and we establish a connection with the finite volume matrix elements. In the two-component models we derive a set of recursion relations for the "magnonic form factors", which are the matrix elements on the nested Bethe Ansatz states. In certain simple cases (involving states with only one spin-impurity) we obtain explicit solutions for the recursion relations.Comment: 34 pages, v2 (minor modifications

Predicting CYP3A-mediated midazolam metabolism in critically ill neonates, infants, children and adults with inflammation and organ failure.

Aims: Inflammation and organ failure have been reported to have an impact on cytochrome P450 (CYP) 3A-mediated clearance of midazolam in critically ill children. Our aim was to evaluate a previously developed population pharmacokinetic model both in critically ill children and other populations, in order to allow the model to be used to guide dosing in clinical practice. Methods: The model was evaluated externally in 136 individuals, including (pre)term neonates, infants, children and adults (body weight 0.77–90 kg, C-reactive protein level 0.1–341 mg l–1 and 0–4 failing organs) using graphical and numerical diagnostics. Results: The pharmacokinetic model predicted midazolam clearance and plasma concentrations without bias in postoperative or critically ill paediatric patients and term neonates [median prediction error (MPE) 180%). Conclusion: The recently published pharmacokinetic model for midazolam, quantifying the influence of maturation, inflammation and organ failure in children, yields unbiased clearance predictions and can therefore be used for dosing instructions in term neonates, children and adults with varying levels of critical illness, including healthy adults, but not for extrapolation to preterm neonates

Learners in a Changing Learning Landscape: Reflections from an Instructional Design Perspective

Van Merriënboer, J. J. G., & Stoyanov, S. (2008). Learners in a changing learning landscape: Reflections from an instructional design perspective. In J. Visser & M. Visser-Valfrey (Eds.), Learners in a changing learning landscape: Reflections from a dialogue on new roles and expectations (pp. 69-90). Dordrecht, The Netherlands: Springer.Both learners and teachers find themselves in a learning landscape that is rapidly changing, along with fast societal and technological developments. This paper discusses the new learning landscape from an instructional design perspective. First, with regard to what is learned, people more than ever need flexible problem-solving and reasoning skills allowing them to deal with new, unfamiliar problem situations in their professional and everyday life. Second, with regard to the context in which learning takes place, learning in technology-rich, informal and professional 24/7 settings is becoming general practice. And third, with regard to the learners themselves, they can more often be characterized as lifelong learners who are mature, bring relevant prior knowledge, and have very heterogeneous expectations and perceptions of learning. High-quality instructional design research should focus on the question which instructional methods and media-method combinations are effective, efficient and appealing in this new learning landscape. Some innovative instructional methods that meet this requirement are discussed

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori